The Edge AI Renaissance

The promise of edge AI lies in its ability to have an immediate impact on real-life problems for businesses, leading to a wide-open field for innovative solution providers.

When Wi-Fi wasn’t cutting it, MSP Step CG helped Toyota with a private 5G solution to improve operations on the manufacturer’s factory floor and power its edge AI applications.

Now there are 20 more industrial and health-care deals with big-name brands in the pipeline in addition to the deal with Toyota for fast-growing Step, according to Ed Walton, CEO of the Covington, Ky.-based company.

“It’s really picking up momentum. Anywhere we’re deploying private cellular, it is enabling edge applications with AI,” Walton said.

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Step has built out a robust private networking practice and has been deploying private cellular networks for its enterprise customers with the help of its technology partners Ericsson and Celona. The MSP is integrating that technology with traditional wired and wireless infrastructure from companies including Hewlett Packard Enterprise and Juniper Networks.

Step has seen a massive uptick in private cellular use cases, especially for industrial environments. Private cellular and 5G, combined with edge computing, are the power behind AI at the edge where fast speeds and low latency are required, Walton said.

Edge AI, or using AI in an edge computing environment to bring the processing of AI functions closer to where the data is being generated, is top of mind for a variety of technology vendors, cloud providers and hardware makers alike. It’s also an emerging area of opportunity for solution providers.

Worldwide spending on edge computing is expected to reach $228 billion in 2024, an increase of 15 percent over 2023, according to IDC. The research firm has further forecast global spending on edge computing to skyrocket over the next several years to $378 billion in 2028.

The Operational Technology Opportunity

Where edge AI will make the largest impact isn’t in the carpeted enterprise. It’s inside the factories, mining operations, airport baggage handling areas and hospital corridors where quick decision-making is required, according to Shahid Ahmed, group executive vice president of new ventures and innovation for solution provider giant NTT Data.

“That’s the world we are living in with edge AI. It’s very different. It has zero time for cloud interaction and has to be done all locally, not only for security reasons, but for latency reasons. That’s what our edge AI does, [and] we think it’s a phenomenal opportunity for this part of the world where we haven’t seen a lot of AI transformation [as opposed to] the carpeted space,” Ahmed said.

To that end, NTT Data in July unveiled its ultralight Edge AI platform, a fully managed service that lets businesses deploy AI applications at the edge using smaller, more efficient language learning models. With U.S. headquarters in Plano, Texas, NTT Data today is working with a number of businesses to improve their operations by employing AI at the edge. Its pipeline for this business is rapidly growing every week, according to Ahmed.

One such manufacturing customer is employing computer vision in the form of a 4K camera that can capture events over the factory’s thousands of square meters in real time. It’s analyzing automated guided vehicles, goods and whether people are wearing safety gear moving through different areas of the floor.

“It’s basically ingesting terabytes of data per hour, and it’s simultaneously taking action and correlating all those actions into a single AI model. It knows if somebody wore a hard hat or didn’t wear a hat, if a door was left open, how many boxes were put onto the pallet and ready to be shipped, all in one single capture, and it’s able to take action simultaneously,” he said. “It’s one single camera. That’s the power of edge AI.”

The same process without computer vision would require separate devices capturing the different areas of the facility, including views of the pallets, people and doors, Ahmed said.

“Not only do you reduce all those sensors and IoT devices and take them out of the picture because you’ve got one single machine vision camera, but it can take immediate action to alert the right people,” he said.

The promise of edge AI lies in its ability to have an immediate impact on real-life problems, Ahmed said.

“I think a lot of focus is on the big AI models at the moment, but none of them really has a way of scaling down to where you can get dynamic, time series data that requires very quick-response times to take actionable measures or provide tools that can immediately provide some benefit to that factory worker that makes their life a lot easier,” he said. “I call this ‘blue-collar AI.’ It’s gritty, it’s raw, and it’s real.”

Edge computing adoption has largely been niche, particularly seen in industries such as retail and manufacturing for real-time applications. But AI at the edge is evolving, and solution providers like World Wide Technology (WWT) are increasingly seeing demand for emerging use cases that can’t tolerate even seconds of latency, including computer vision and safety in manufacturing.

“If I’m a user executing a chatbot prompt, and my interface to that is text in a browser, I don’t really care if that takes one second or 10 seconds. I don’t really need edge AI. It can go back to wherever—the cloud or the data center—wherever I’ve got my LLM inferencing,” said Neil Anderson, vice president of cloud, infrastructure and AI solutions for St. Louis-based WWT. “But if I’m trying to do something like computer vision where I’ve got some cameras locally in a store where I’m trying to do facial recognition or customer behavior recognition, that’s where you really need something at the edge where you can process that locally. You’re not going to want to drag all that video back to a central place to process it. You need a distributed approach.”

WWT has helped customers implement safety measures in industrial manufacturing settings.

“[The customer might say,] ‘I want to use computer vision for security and to keep people safe.’ If they’re sticking an arm somewhere they shouldn’t be, we can detect that and stop the machine. That requires ultra-fast response time, so you’re going to do that at the edge,” Anderson said.

It’s critical that solution providers understand the specific use case their customers have in mind. From there, they can determine the architecture requirements, Anderson said.

WWT, an AI-first company according to its co-founder and CEO Jim Kavanaugh, offers AI workshops for customers. The first thing that’s discussed is the use case, Anderson said.

“‘What are you trying to do?’ The second thing is, ‘OK, what does the architecture look like to be successful to bring that use case to life?’” he said.

Platform manufacturers are especially keen on edge AI to help offload features that don’t have to be processed in the cloud or a data center. Cisco Systems’ Webex platform for collaboration, as an example, has an AI-powered background noise removal feature that automatically filters unwanted or unexpected noise for users.

The San Jose, Calif.-based tech giant also partnered with Nvidia in 2023 for the creation of the Room Kit EQX, a packaged offering for medium and large conference rooms that consolidates audio, video and compute components into a single unit that’s powered by Nvidia’s Jetson edge AI platform and is bringing advanced AI capabilities to Cisco’s portfolio of collaboration devices.

Securing The Edge

Security concerns abound for edge AI, simply because it’s yet another attack vector that can be exploited, solution providers said.

“To the extent that you’re putting data on devices or you’re putting data at the edge, you have to be protecting that data wherever it is, otherwise you’re at risk,” said Steve Wylie, senior vice president and general manager, East, for Irvine, Calif.-based solution provider giant Trace3.

As excitement and momentum builds around potential game-changing use cases, there are steps that enterprises need to take now to prepare for when they do have a viable edge AI opportunity, he said.

“They need to know, ‘Where’s your data? Is it in a centralized place, or at least, do you know what’s where, and is it clean? Can you access it?’ There are things that people can be doing with their environments to prepare themselves so that when they have a viable use case, they can act on that use case,” Wylie said.

Trace3 has placed a big bet on AI, as evidenced by upward of 80 percent of the team having been trained on it. The businesses that are thinking about AI the right way are doing some level of planning now with the help of Trace3, Wylie said.

“Our first step was getting the team to speak the language of AI. The next piece is then helping clients speak the language of AI. The whole reason for doing that is so people can then understand the real capabilities of AI, what they can really do with it, and preparing so they can put their business in a place to be ready to adopt those use cases when their business is ready to consume them,” he said.

Wylie and his team are doing red-teaming exercises to show clients where their cybersecurity gaps are related to AI technology, especially in a distributed environment or network edge, he said.

“We’re showing clients [why] you can’t just implement [Microsoft] Copilot out of the box and just hope that it’s secure. If you’re not fine-tuning it to your environment and making sure holes are closed, you’re putting yourself at risk,” he said. “With any of these capabilities, you’re opening up access to a data lake, or access into some element of your environment that has a ton of your data. You can really get the keys to the kingdom.”

Putting The Pieces Together

Ericsson Enterprise Wireless Solutions, which now includes Cradlepoint, is focused on building AI into the network. AI requires data, and to get that data to the edge, good connectivity is king, said Donna Johnson, head of marketing for Plano, Texas-based Ericsson Enterprise Wireless Solutions.

Similar to solution providers, manufacturing is a popular use case that Cradlepoint is seeing where there is a desire for edge computing using AI. The problem is, connectivity usually isn’t sufficient to send the data back to the data center or a cloud for processing at many manufacturing facilities, Johnson said.

“The connectivity often isn’t high-performance enough, and it’s subject to intermittent outages, even micro-outages in the Wi-Fi network. Our work on private 5G is a great example of how we’re building a really high-performance, high-capacity network that allows us to collect all these data points from the manufacturing process, at the edge, and do analysis on it that allows [businesses] to actually correct potential errors and find safety issues in real time, and hold on to the data for later analysis for potential issues,” she said.

It’s a huge ask from customers, and a big opportunity for solution providers, Johnson said.

“No one company has the full solution here, so I think that integration role is going to be huge for the channel to be able to bring the different pieces together in a unique way for individual customers,” she said.

As executives read about edge AI and learn how it could work for their businesses, it’s been “call after call” for Albertson, N.Y.-based solution provider Vandis, according to CTO Ryan Young.

Many customers, especially those in the manufacturing space, have been running AI in centralized data lakes like Microsoft Azure, Amazon Web Services or Google Cloud, but the costs associated with running AI in those environments are becoming too high. These businesses are looking to defray those costs by putting AI at the edge. By moving to the edge, there’s also the added benefit of modernizing what were once very “low-tech” networks, Young said.

“Historically, manufacturing has had very low-throughput networks that are really just there to support the barcode scanners and forklifts. Now, all of a sudden, it’s, ‘We’re going to put compute out there, and we need at least a 10-Gig backbone and we need 10-Gig ports to connect the compute.’ We’re seeing those early customers building the foundation,” he said.

Vandis is evaluating its customers’ data to determine what they need and how they can piece together a solution, he said.

“[HPE] Aruba [Networking] has a product. Juniper has a product with Mist, Palo Alto [Networks] and Fortinet have products, but these are point products. The overall data is not being brought together and completed,” he said. “The journey we’re on right now with our customers is completing their data.”

To do that, Vandis is using ReadyWorks, a low-code, agentless SaaS platform to pull data across disconnected systems into one source.

“When you’re looking at manufacturing or shipping or supply line constraints, you have all the data there for the AI to be able to make decisions on,” he said. “We’re starting to work with these [customers] on that completeness of data so that when they’re ready to make that next step, they’re in a really solid position to actually run AI models against that data set.”

That’s where vendors like HPE, with its edge AI clusters, will come into play, he added.

“They’re preloaded with trained GenAI models that you just pump your data into and start your query,” Young said. “I think that’s going to be kind of an easy button for a lot of our customers who don’t have the skill set or the wherewithal to go and build their own AI models.”